Method and system for 3d capture based on structure from motion with simplified pose detection

ABSTRACT

Method and System for 3D capture based on SFM with simplified pose detection is disclosed. This invention provides a straightforward method to directly track the camera&#39;s motion (pose detection) thereby removing a substantial portion of the computing load needed to build the 3D model from a sequence of images.

RELATED APPLICATION

This application is a utility application claiming priority of U.S. provisional application(s) Ser. No. 61/732,636 filed on 3 Dec. 2012; Ser. No. 61/862,803 filed 6 Aug. 2013; and Ser. No. 61/903,177 filed 12 Nov. 2013 Ser. No. 61/948,401 filed on 5 Mar. 2014; U.S. Utility application Ser. No. 13/861,534 filed on 12 Apr. 2013; and Ser. No. 13/861,685 filed on 12 Apr. 2013; and Ser. No. 14/308,874 filed Jun. 19, 2014 and Ser. No. 14/452,937 filed 6 Aug. 2013; Ser. No. 14/539,924 filed 12 Nov. 2004 and PCT Application PCT/US15/19040 filed 5 Mar. 2015.

TECHNICAL FIELD OF THE INVENTION

The present invention generally relates to optical systems, more specifically to electro-optical systems that are used to determine the camera orientation and position (collectively known as pose) and capture 3D models relative to the photographed scene in order to extract correct dimensions and positions of physical objects from photographic images.

BACKGROUND OF THE INVENTION

The task of capturing the 3D information in a scene consists of first acquiring a set of range measurements from the measurement device(s) to each point in the scene, then converting these device-centric range measurements into a set of point locations on a single common coordinate system often referred to as “world coordinates”. Methods to acquire the range measurements may rely heavily on hardware such as 2D time-of-flight laser rangefinder systems which directly measure the ranges to an array of points within the measurement field-of-view. Other systems exist that rely heavily on computing power to determine ranges from a sequence of images as a camera is moved around the object or scene of interest. These later systems are commonly called Structure From Motion systems or SFM. Hardware-intensive solutions have the disadvantages of being bulky and expensive. SFM systems have the disadvantage of requiring extensive computing resources or extended processing times in order to create the 3D representation, thus making them unsuitable for small mobile consumer devices such as smart phones.

Existing Structure from Motion (SFM) systems involve two computation paths, one to track the pose (orientation and position) of the camera as it captures a sequence of 2D images, the other to create a 3D map of the object or environment the camera is moving in or around. These two paths are interdependent in that it is difficult to track the motion (pose) of the camera without some knowledge of the 3D environment through which it is moving, and it is difficult to create a map of the environment from a series of moving camera images without some knowledge of the motion (pose) of the camera.

This invention introduces a method and system for capturing 3D objects and environments that is based on the SFM methodology, but with the addition of a simplified method to track the pose of the camera. This greatly reduces the computational burden and provides a 3D acquisition solution that is compatible with low-computing-power mobile devices. This invention provides a straightforward method to directly track the camera's motion (pose detection) thereby removing a substantial portion of the computing load needed to build the 3D model from a sequence of images.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of the present invention and the advantages thereof, reference is now made to the following description taken in conjunction with the accompanying drawings in which like reference numerals indicate like features and wherein:

FIG. 1 illustrates an embodiment of the 3D capture system in the process of capturing a sequence of images used to create the 3D model of an object;

FIG. 2 illustrates examples of reference templates used with the embodiment illustrated in FIG. 1;

FIG. 3 illustrates a block diagram of major architecture components of an embodiment of the system; and

FIG. 4 illustrates the processing flow of the 3D mapping system;

DETAILED DESCRIPTION OF THE INVENTION

Preferred embodiments of the present invention are illustrated in the FIGUREs, like numerals being used to refer to like and corresponding parts of the various drawings.

Before proceeding with the description it would be helpful to define a few terms for the purpose of this written specification. Some of these terms are used loosely from time to time and may mean different things. For example the term “pose” of the camera is sometimes used to refer to the “orientation” of the camera independent of the “position” of the camera. In other cases “pose” is used to include both the orientation and the position of the camera. Sometimes the context makes it clear; sometimes it does not. In this specification the distinction of the two meanings is important so we provide clarifying definitions.

Glossary

3D Mapping or 3D Modeling means a 3D model of an object or scene in world coordinates from which accurate measurements may be derived.

Orientation means the direction in which the central optical axis of the camera is pointed.

Pose means the orientation and position of the camera.

Position means the location of the camera relative to the fixed origin of the world coordinates used in the system.

Proportional 3D Model means a relational data set of the spatial relationship of key features of an object or scene where the scale or absolute size of the model is arbitrary and unspecified.

Range means distance from the point of observation to the point being observed.

SFM means Structure From Motion as further described below.

World Coordinates is a fixed coordinate system for the 3D Mapping which has an absolute position, orientation and scale relative to the scene or object being captured and from which physical measurements of constituent parts of the 3D scene can be extracted.

Structure From Motion (SFM) is a well known technique or category of techniques for determining the 3D mapping of a scene from a sequence of 2D images. Each point of the object or environment being mapped is captured in a minimum of two 2D images. SFM uses the principle of triangulation to determine the range to any point based on how the position of the point shifts in the 2D image from one camera position to another. It is necessary to accurately know the pose (position and angle) of the camera for each of the 2D images in order to be able to correctly calculate the range. Given a sufficient number of 2D views of the scene, existing SFM techniques can determine the pose of the camera in order to create a structural map of the object or scene. However, reliance on the image content alone for determining both the pose of the camera and then a structural model and or structural map is computationally intensive.

Existing SFM methods, whether feature-based or optical-flow-based, must determine corresponding pixels between each video frame in order to track the camera motion. For example, Klein and Murray (“KM”) (identified below) start by creating an initial 3D map based roughly on a stereo pair of images and then estimate the camera's pose relative to this map. For each new frame of video, KM first extrapolates from the camera's prior motion to estimate the camera's new pose. Based on that assumed pose, KM calculates where key scene features should have moved to in the new image, followed by detecting these features in the new image and adjusting the pose to match the image. KM runs the tracking and mapping operations in parallel extensively and intensively using a Graphical Processing Unit (“GPU”) and a Central Processing Unit (“CPU”).

The present 3D Mapping System uses an SFM engine but relieves the SFM engine from having to use this iterative technique for determining the pose of the camera for each image, thereby removing a substantial portion of the computing load needed to build the 3D model from a sequence of images and allowing for the potential for much more accurate results.

The improved mapping system can also be used to produce a non-contact measurement tool and profiler, a robotic vision module, an artificial vision system, and products to help the visually impaired. The products could be integrated as Apps and/or accessories with existing mobile devices such as smart phones, tablets, or notebook computers, or as a stand-alone product. In another embodiment, the digital camera could remain stationary while the scene being recorded is moved to expose views from multiple viewpoints.

Turning now to FIG. 1, the embodiment of the Mapping System is incorporated in a mobile device 10 which includes a digital camera (not shown)—A standard digital video camera or digital still camera which may be integrated into a smart phone, tablet computer or may be a stand-alone accessory or image capture device. The embodiment illustrated in FIG. 1 also illustrates the use of known reference template patterns 12, 14, 16, 18. Note that in FIG. 1 several different known reference templates are employed. In other embodiments reference templates different from those shown may be employed. In other embodiments several copies of the same template may be employed. In the embodiment shown, the reference templates 12, 14, 16, 18 are on a pedestal 20 whose geometry (3D structure) may or may not be known. If the pedestal has a known geometry, in some embodiments this known geometry may be used by the template pattern recognition engine and/or the pose engine and/or the SFM engine and or the mapping engine (each engine further discussed below). However, in the present embodiment the geometry of the pedestal 20 is not necessarily known and is not used.

The object to be mapped in the embodiment illustrated in FIG. 1 is the cat statue 22. FIG. 1 illustrates four different camera pose images in the four outer corners of the illustration and a central top view showing a possible motion pattern 30 of the mapping system 10 in a path 30 around the object 22 to be mapped. Although the image illustrates a circular path of motion 30, it is not necessary that the motion path of the camera be regular. In fact it can be irregular and the irregularity may give better results. This applies to all elements of the change in pose of the camera, both positional change relative to the object and also to the change in orientation of the camera.

FIG. 2 illustrates in greater detail various acceptable reference templates. Many others are possible.

FIG. 3 illustrates a block diagram of major architecture components of an embodiment of the system and the relationship between the components and processing elements used in the embodiment illustrated in FIG. 1. The embodiment 10 includes a digital camera 50 capable of capturing video or some other sequence of images. The embodiment also includes a template engine 52 which recognizes the reference template pattern(s) or known reference objects in the digital image(s) and a pose engine 54 which calculates the orientation and position of the camera for each image using data received from the template engine 52.

The use of a reference template or templates placed into the scene very simply and directly provides the camera pose and scale. The exact geometry of the reference template(s) or reference object(s) is known by the pose detection processor. There is great flexibility on the design of the reference template(s). Rather than, in addition to, or in combination with reference templates a plethora of different reference object(s) may also be used. The reference templates or objects could be 2D pattern(s) or 3D reference objects. The pedestal 20 on which the target object is placed may be a reference object. The reference templates or objects in this specification should not be considered limiting in terms of the design of reference templates or objects. There are an unlimited number of design possibilities. The essential requirement is that the Pose Engine 54 has knowledge of the reference objects or templates. In processing, the system will recognize and identify key features (fiducials) of the reference objects in each image within the sequence of captured images. Therefore, it is advantageous that the reference template/object be chosen for providing speed and ease of recognition. When more than one reference template/object are used, it is not necessary that the templates all be different or that they all be the same pattern. Any combination is acceptable.

The pose engine 54 used in this embodiment compares the known geometry of the reference template with the geometry detected in each image. From this comparison the pose engine 54 determines the camera pose including both the position and orientation of the camera relative to the reference template/object.

The embodiment illustrated in FIG. 3 also includes an SFM engine 56. In this embodiment the SFM engine uses image data captured by the camera together with pose data from the pose engine. Thus the computational complexity of using an SFM engine to calculate camera pose has been avoided. A preferred embodiment for the SFM engine is based on optical flow. The SFM engine 56 operates on pairs of 2D images taken by the camera from different poses. Typically the SFM engine 56 operations are performed on pairs of adjacent images (adjacent in a capture sequence) although non-adjacent images could also be used. For each pair of images, the motion of points in the scene is estimated using an automatic optical flow calculation. Then, using the camera pose determined by the Pose Engine, the range to points in the scene is calculated from the optical flow field. Alternatively, there are a number of other SFM techniques for generating a 3D mapping.

For example See:

-   Klein, G., & Murray, D. of Oxford, UK: Active Vision Laboratory,     University of Oxford, “Parallel Tracking and Mapping for Small AR     Workspaces”, published in International Symposium on Mixed and     Augmented Reality (2007); -   Newcombe, R. A., et. al. of London, UK: Department of Computing,     Imperial College, “DTAM: Dense Tracking and Mapping in Real Time”,     published in International Conference on Computer Vision, (2011); -   Zucchelli, M., of Stockholm: Royal Institute of Technology,     Computational Vision and Active Perception Laboratory, “Optical Flow     based Structure from Motion”, Doctoral Dissertation (2002) Note:     Zucchelli, M describes the use of optical flow in SFM using a more     computationally intensive indirect method to estimate the camera's     pose compared to the direct camera pose measurement used here; and -   Petri Tanskanen, et. al., of Zurich, Swiss Federal Institute of     Technology (ETH), “Live Metric 3D Reconstruction on Mobile Phones”,     published in International Conference on Computer Vision, (2013).

Optical Flow is a technique used to track the movement of points or features in a scene from one image of the scene to another. Mathematically, this can be described as follows. Given a point [u_(x), u_(y)] in image I₁, find the point [u_(x)+δ_(x), u_(y)+δ_(y)] in image I₂ that minimizes the error ε in a neighborhood around the point, i.e., minimize

${ɛ\left( {\delta_{x},\delta_{y}} \right)} = {\sum\limits_{x = {u_{x} - w_{x}}}^{u_{x} + w_{x}}\; {\sum\limits_{y = {u_{y} - w_{y}}}^{u_{y} + w_{y}}\; {\left( {{I_{1}\left( {x,y} \right)} - {I_{2}\left( {{x + \delta_{x}},{y + \delta_{y}}} \right)}} \right).}}}$

This technique was originally developed by Horn and Schunck as described in the following reference.

See Berthold K. P. Horn and Brian G. Schunck, “Determining optical Flow: a retrospective”, Artificial Intelligence, 17:185-203, 1981.

In the present invention, optical flow is one choice of techniques used to determine the motion of features in the scene from one image to the next in the series of images. The result of this optical flow computation is combined with the camera pose information obtained from the pose engine to calculate the distance to points in the scene based on SFM triangulation.

In the present embodiment discussed, a 3D modeling engine 58 converts the 3D mapping output from the SFM engine 56 in local camera coordinates into a 3D model in world coordinates. The 3D Modeling engine takes the 3D map points generated in local camera coordinates by the SFM engine and assembles the data from the complete sequence of 2D images into a single 3D model of the scene in world coordinates. It uses the ranges from the SFM Engine along with the known camera pose for each local range data set to map the points into the fixed world coordinates. This is done with a coordinate transformation whose transformation matrix values are determined from the pose and that maps the points from local camera coordinates into world coordinates. The 3D Modeling Engine also may include data processing routines for rejecting outliers and filtering the data to find the model that best fits the collection of data points.

Finally the Modeling device contains User Interface and Data Storage functions that provides the user with choices as to how the 3D model and data is to be displayed and stored. The user can request a printer-ready 3D file, dimensional measurements extracted from the model, as well as other operations specific to the application. The 3D model is stored together with application specific data in a Quantified Image/Data File.

FIG. 4 illustrates the operational flow 100 of using the 3D Capture and Modeling System embodiment illustrated in FIG. 1 and FIG. 3. The first step 102 is placement of reference object(s)/template(s) into the scene or in proximity to the object to be modeled. The next step 104 should be anticipated when the template(s)/object(s) were placed. They should be strategically placed so that at least one reference template/object is in the camera's view through the planned path of the camera in step 104. It is also preferable that each reference template/object be imaged with at least one other reference template/object in at least one 2D image. In some cases, the scene may already naturally contain a known reference object or objects such that it is not necessary to specifically place additional reference objects/templates into the scene. After the reference template(s)/object(s) are placed, the camera is employed to take a video or other sequence of images of the scene or object from a variety of camera poses (position(s) and orientations) 104. In other words, the camera is moved around scanning the scene or object from many viewpoints. The objective of the movement is to capture views of the scene or object from all viewpoints necessary to build a complete 3D model. It should be noted that the efficiency of creating the 3D model and accuracy of 3D model are dependent on the captured images. Less data may result in greater efficiency but less accuracy. More data may be less efficient but provide more accuracy. After a certain amount of data, more data will result in diminishing returns in increased accuracy. Depending on the scene or object to be modeled, different movement paths will result in greater efficiency and accuracy. Generally, a greater number of camera images (video frames) from a wide range of camera poses should be used on areas of the object or scene where accuracy is of the greatest interest.

Once at least the first image is captured, the template pattern recognition engine can begin to recognize template patterns 106. Once the template pattern recognition engine has recognized a first template pattern 106, the pose engine can determine the orientation and position of the camera relative to the recognized template pattern 108. The origin of the world coordinate system is often a specific point on the first reference template or has a known position relative to the first template. When more than one template is used, the position of each template relative to the first template is determined from images containing more than one template in such a way that camera pose is always referenced to the world coordinate origin.

Once the camera pose has been determined for two or more images, the SFM engine can begin to gather data for generating a structural mapping of the scene 110. The SFM Engine automatically detects points of interest in the images of the object or scene and estimates the motion of the points of interest from one image to the next using optical flow or other known SFM techniques thus building a structural mapping of the object or scene. The 3D Modeling Engine then takes the structural model estimates from the SFM engine output and the pose information (camera position and orientation) from the output of the Pose Engine to begin creating a 3D model of the object or scene in world coordinates 112. The 3D Modeling Engine weights the information and makes determinations as to the best fit of the available data. The 3D Modeling Engine also monitors the progression of changes to the model as new information is evaluated. Based on this progression, the 3D modeling engine can estimate that certain accuracy thresholds have been met and that continued processing or gathering of data would have diminishing returns 114. It may then, depending on the application, notify the user that the user selected accuracy threshold has been achieved. The resultant 3D model is saved and the user is provided with a display of the results and provided with a user interface that allows the user to request and receive specific measurement data regarding the modeled object or scene 116 and 118.

While the disclosure has been described with respect to a limited number of embodiments, those skilled in the art, having benefit of this disclosure, will appreciate that other embodiments may be devised which do not depart from the scope of the disclosure as disclosed herein. The disclosure has been described in detail, it should be understood that various changes, substitutions and alterations can be made hereto without departing from the spirit and scope of the disclosure. 

1. A 3D modeling device comprising a camera which captures a sequence of images from a sequence of poses relative to a known reference template(s) placed into the scene, or relative to known reference object(s) in the scene to be modeled; a template pattern recognition engine which recognizes a reference template pattern placed in a scene to be mapped and determines the position of known points on the image of the reference template/object and a pose engine which determines the orientation and position of the camera relative to the template or templates recognized by the template pattern recognition engine; a SFM (Structure from Motion) engine which tracks the relative movement of features in a scene from one image to another image and determines the range to points in the scene from the viewpoint of the camera that captured the images; a 3D modeling engine which combines product from the template pattern recognition and pose engines with product from the SFM engine to provide dimensionally correct 3D map output of the scene.
 2. The 3D modeling device of claim 1 where the reference template has at least four co-planar reference fiducials.
 3. The 3D modeling device of claim 1 where the reference template is a printed pattern.
 4. The 3D modeling device of claim 3 where the reference template is a sticker which can be affixed to objects in the scene.
 5. The 3D modeling device of claim 3 where the pattern includes color(s) which can more easily be extracted via a digital filter.
 6. The 3D modeling device of claim 1 where the reference template includes a pattern of electromagnetic emitting elements.
 7. The 3D modeling device of claim 1 where a series of reference templates are placed into the scene or near the object to be modeled.
 8. The 3D modeling device of claim 3 where a plurality of the reference templates have at least four co-planar reference fiducials.
 9. The 3D modeling device of claim 1 where the data processing system also creates a Quantified Data File which includes the 3D model and automatically generated measurement data.
 10. The 3D modeling device of claim 1 where the sequence of poses is fully or partially achieved by moving the object(s) to be modeled.
 11. The 3D modeling device of claim 1 where SFM engine tracks motion of features in the scene from one image to the next using optical flow.
 12. The 3D modeling device of claim 1 where the 3D modeling engine detects and rejects outlier data to find a model that best fits the collection of data.
 13. The 3D modeling device of claim 12 where the 3D modeling engine uses data filtering to reject outlier data to find a model that best fits the collection of data.
 14. The 3D modeling device of claim 1 where the user can query the modeling device to receive specific dimensional measurements from the 3D model.
 15. The 3D modeling device of claim 14 where the 3D model is stored together with specific measurement data automatically extracted and measurement data of parameters which were manually identified from the 3D model in a quantified image/data file.
 16. The 3D modeling device of claim 1 where the dimensionally correct 3D model of the scene/object are in a world coordinate system with an origin that is a specific point relative to the reference template.
 17. The 3D modeling device of claim 16 where the origin of the world coordinate system is a specific point relative to the first template detected.
 18. The 3D modeling device of claim 1 where the dimensionally correct 3D model modeling of the scene/object is monitored to estimate the accuracy of the model as more data is captured.
 19. The 3D modeling device of claim 18 where the user is notified that the estimate accuracy threshold of the 3D model has been reached. 